Decoding finger movements from ECoG signals using switching linear models

TL;DR

This paper introduces a switching linear model approach to decode finger movements from ECoG signals, improving prediction accuracy by modeling the complex relationship as an ensemble of simpler linear models.

Contribution

The paper presents a novel switching linear model framework for decoding finger movements from ECoG signals, addressing the complexity of the signal-movement relationship.

Findings

Switching models improve decoding accuracy.

Ensemble of linear models simplifies complex relationships.

Effective prediction of 5-D finger trajectories.

Abstract

One of the major challenges of ECoG-based Brain-Machine Interfaces is the movement prediction of a human subject. Several methods exist to predict an arm 2-D trajectory. The fourth BCI Competition gives a dataset in which the aim is to predict individual finger movements (5-D trajectory). The difficulty lies in the fact that there is no simple relation between ECoG signals and finger movement. We propose in this paper to decode finger flexions using switching models. This method permits to simplify the system as it is now described as an ensemble of linear models depending on an internal state. We show that an interesting accuracy prediction can be obtained by such a model.